Project B3 (finished)

Background and Motivation

Many relevant applications in signal detection rely on the separation of sources from a mixture of signals without a prior knowledge about the mixing process. For instance, for audio signals, the analysis and separation into their source components is an important tool for the extraction of metadata from audio data, e.g. for separating musical instruments from a polyphonic ensemble, for music restoration, or, for the extraction of speech from a noisy background. Data recorded from a single-channel are often characterized by their high dimensionality. Therefore, effective dimensionality reduction methods are essentially needed.

Aims and Objectives

Our research will focus on the application and investigation of novel dimensionality reduction methods in Independent Subspace Analysis (ISA) for signal detection and separation. In signal detection, the time locations at which a certain source signal is active are to be identified. This is a rather important task, since it provides relevant information about a mixture of signals. These information can be used to reduce the computational complexity in subsequent steps of the signal analysis. In fact, provided that the time locations, where a certain source is active, are known, signal separation algorithms could better explore these regions to perform the source extraction at higher resolution. To further improve such strategies, their mathematical understanding is supported by numerical simulations. Among other applications, we consider the signal detection problem in a complex mixture of transitory acoustic sounds.